6G-SANDBOX 3rd OPEN CALL WINNERS

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During the Open Call Period (deadline: 19th September 2024), we received a total of 36 proposals.

The proposals came from the following countries (Graph 1) and organization types (Graph 2).

Out of the 36 submitted proposals, 14 were accepted, resulting in a 39% success rate.

The accepted proposals originate from the following countries (Pie Chart 1, based on the lead organization’s country) and organization types (Pie Chart 2).

Open Call 2 Innovative Experiments – Winners per Project Proposal

NTN Wave: NTN Waveform Performance Analysis

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This project presents a comparative experimental study of 5G New Radio (5G NR) and DVB-RCS2 waveforms for their suitability in non-terrestrial networks (NTN) operating in geostationary (GEO) satellite scenarios. Using open-source platforms such as the OpenAirInterface5G NTN suite for 5G NR and openSAND for DVB-RCS2, the experiment evaluates each technology’s performance under realistic traffic conditions including video streaming, file transfers, and interactive applications. Performance is assessed across key metrics such as throughput, latency, jitter, and error rates, under various system configurations and channel conditions including large GEO delays and different signal-to-noise ratios. The novelty of this work lies in its cross-layer, end-to-end emulation using publicly available tools. It addresses key limitations in prior research that either focused solely on physical layer simulations or relied on proprietary platforms. This is one of the first experiments to directly compare DVB-RCS2 and 5G NR using open tools under realistic conditions, offering practical and reproducible insights into their respective strengths, weaknesses, and potential synergies. The results of this experiment will help identify which technology works best under specific conditions. They will also provide useful information for designing future networks that combine satellite and terrestrial systems. The information developed will be shared openly with the research community.

6G-VLCBOX: Scalable applications with point-to-multipoint communication towards 6G/IMT-2030 technologies

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6G-VLCBOX will on-board, experiment with and validate an end-to-end 3GPP Rel-18 system incorporating, for the first time and in an open-source environment, 5G Multicast-Broadcast Services (MBS) features for reliable, low latency, and scalable communication. The open-source software components for MBS contribute towards the SNS Stream C goals of advancing on open architectures and APIs while providing full customization and control for 6G-SANDBOX members and the community. The ability to customize code and configuration is of particular relevance given that MBS spans the UE, RAN (gNB), 5G Core (5GC) and User Services architecture. The project will develop the necessary 3GPP features to enable MBS by extending existing open-source initiatives: For the MBS RAN and UE, the srsRAN platform will be further developed to integrate point-to-multipoint NR features; while for the MBS functions in the 5GC, the relevant control and user plane network functions will be extended from the Open5GS core implementation. This means to update both segments of the solution to Rel-17 and Rel-18 functionalities. The solutions will be integrated into the Malaga’s platform, which already features SDR (software defined radio) equipment and Open5GS, with the objective to create a new type of Broadcast-enabled Trial Network. The developments of the project will be aligned with the efforts of the 5G-MAG Reference Tools development programme, which fills the gaps between the development of specifications for multimedia applications and their conformance testing, validation and prototyping. Moreover, the project will contribute feedback to 3GPP as part of 5G-MAG’s activities as 3GPP Market Representative Partner. Additionally, 6G-VLCBOX will also provide insight into MBS applications for media, public safety and its potential integration with Non-Terrestrial Networks. With the tools developed, the project will realize the first End-to-End (E2E) experiment with MBS in an European test-bed, as these point-to-multipoint communication features introduced starting from 3GPP Rel-17 have not been trialed to date nor implemented in any open-source environment available to the community. UPV is currently the main developer and maintainer of MBS open-source software in the 5G-MAG Reference Tools. UPV is the sole participant of 6G-VLCBOX, and will ensure the success of this project with its long track and experience in the design and development of point-to-multipoint features from previous projects such as 5G-PPP FUDGE-5G; and other 3GPP point-to-multipoint solutions such as 5G Broadcast, as shown in the 5G-PPP projects 5G-TOURS and 5G-Xcast. The proposal is very well aligned to the general objective of the 3rd 6G-SANDBOX open-call: Innovative experiments deployed and tested over the 6G-SANDBOX experimentation infrastructure. 6G-VLCBOX will provide 6G-SANDBOX and the Malaga testbed with a suite of scalable applications point-to-multipoint communication towards 6G/IMT-2030 technologies that can enable bandwidth constrained applications and provide continuous support after the open-call is over.

OVQOS: Orchestration of Virtualized Passive QoS Measurement

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Kaitotek augments the selection of measurement solutions available in the 6G-SANDBOX Trial Networks by bringing the capability to carry out passive QoS measurement with their Qosium solution. Qosium is a real-time and passive network quality measurement software. It enables accurate one-way measurement of QoS for any real application, IP or Ethernet-based, without synthetic test traffic. Qosium tells exactly how the advancements, new innovations and developments, and changes in the test networks affect the performance experienced by different applications, being indispensable information for scientific and R&D work. In the OC project, Kaitotek’s main goal is to integrate Qosium into the 6G-SANDBOX Trial Network concept by making it available through the 6G Library for the Trial Network users to select, deploy, and use in their experiments. Besides integration, technological improvements to Qosium to better satisfy the dynamic orchestration, measurement control, and results collection in Trial Networks are done.

OAIBOX+RIS: Testing the integration of an open-source 5G FR2 gNB with the 6G-SANDBOX Reconfigurable Intelligent Surface

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The main objective of the 6G-SANDBOX OC3 project is to test and validate the 6G-SANDBOX Reconfigurable Intelligent Surface (RIS) technology, developed by Queen’s University Belfast (QUB) and available at the University of Malaga. We will test the integration of the QUB’s RIS with the OAIBOX mmWave and commercial UEs in several propagation scenarios.

ARMOR: Adversarial Resistance and Model Optimization for Robustness for 6G Open Radio Access Networks

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The ARMOR (Adversarial Resistance and Model Optimization for Robustness) project aims to fortify the security and reliability of AI-driven decision-making in 6G Open Radio Access Networks (O-RAN). By developing a comprehensive adversarial testing framework, ARMOR evaluates the vulnerabilities of machine learning models under various attack scenarios throughout the AI lifecycle. The experiment integrates edge AI services on UCD’s O-RAN testbed and the 6G-SANDBOX 5G test platform at Oulu to conduct rigorous real-world validations. Leveraging Large Language Models (LLMs), the project provides intelligible insights and actionable recommendations for stakeholders. The outcomes will guide the design of resilient AI architectures, enabling secure deployment of next-generation 6G networks while enhancing the capabilities of the 6G-SANDBOX ecosystem.

NEXT-CELL-GNN: Next cell prediction using Graph Neural Networks

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As mobile networks evolve in complexity and flexibility, Intelligent Operation Networks (IONs) have emerged as a pivotal enabler for optimizing network services and applications. A key area where IONs can significantly improve performance is handover management—ensuring seamless connectivity and quality of service (QoS) as users move across cells. Traditional handover decisions rely on Layer 1, 2, and 3 metrics, but they face limitations such as handover failures and ping-pong effects. To address these challenges, the NEXT-CELL-GNN project proposes a novel approach leveraging Graph Neural Networks (GNNs) to predict user mobility patterns and enable proactive, intelligent handover decisions. Recognizing that telecommunication networks inherently form graph structures, this project explores how to transform raw network metrics into graph data, identify meaningful node features, and adapt state-of-the-art GNN models (e.g., TGN, GraphMixer) for high-accuracy, low-latency predictions. NEXT-CELL-GNN aims to advance scalable and efficient GNN architectures tailored to the stringent performance needs of 6G networks, paving the way for smarter, AI-driven mobile connectivity.

6G4Artifacts

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The 6G4Artifacts project introduces an innovative remote-controlled robotic system designed for the safe handling and movement of ancient artifacts with precision and without damaging them. By leveraging 5G-enabled teleoperation, robotics, and immersive VR interfaces, the system ensures precise and delicate manipulation, minimizing the risk of damage to fragile cultural artifacts. The project sets the foundation for future applications, including automated artifact restoration and conservation, expanding the role of robotics in cultural heritage management.

6G-VIZ: 6G Real-Time Visualization and Digital Twin Platform

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Next-generation 6G networks aim to revolutionize wireless communication by enabling advanced use cases such as holographic communication, tactile Internet, digital twins, and extended reality (XR). However, developing and validating networks for these applications is challenging due to uncertainties in future technological developments and the difficulty of visualizing complex radio behaviors and network performance. To address these challenges, we propose a modular, open-source, real-time 3D visualization platform that integrates real or simulated use cases into a digital twin of a 6G laboratory environment. This system allows researchers to observe and interact with network KPIs—such as latency, throughput, and signal strength—through immersive simulations that reflect their real-world impact on perceived Quality of Service (QoS). The platform features a 3D globe UI for multi-site collaboration, XR headset support via WebXR, and interactive plug-in-based use case simulations (e.g., robot control, media streaming). It also supports multi-user interaction, live experimentation, and replay capabilities using ROS2 and OpenVidu frameworks. By making invisible network phenomena visible and tangible, this solution enhances research collaboration, stakeholder communication, and training in future network technologies

HART: Health Awareness via Radio Technology

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The HART project explores the integration of advanced wireless technologies and AI-driven health monitoring within future 6G eHealth infrastructures. Central to this collaboration between the start-up Sykno GmbH and the Huawei Munich Research Center is the vertical experimentation of contactless vital sign monitoring using millimeter-wave Radio Sensing (WRS), leveraging Sykno’s ViRa24 system. This technology enables fully non-invasive measurement of physiological parameters by emitting mmWave signals that penetrate clothing and reflect off the body’s surface. AI algorithms are used to extract detailed vital signs from the measured patient movements, including respiratory patterns, heart rate, heart rate variability, and even heart sounds. A typical deployment scenario includes a hospital waiting room, where multiple patients are anonymously monitored by WRS nodes. Data collected is transmitted via 5G/6G networks to an edge/cloud-based eHealth platform, where AI models analyze it in real time to detect abnormal health parameters and alert medical staff promptly. Additionally, Key Value Indicators (KVIs) and Key Performance Indicators (KPIs) related to 6G network performance – such as latency, reliability, service continuity, and user impact – are continuously monitored. By ensuring that no biometric or personal identity data is collected, the solution maintains strong privacy standards, enabling streamlined deployment and regulatory compliance. HART sets a foundational path for scalable, intelligent, and privacy-conscious 6G-enabled eHealth systems. The necessary network infrastructure is explored through vertical trials in the 6G-SANDBOX facility.

BENSM: Blockchain Enabled End-to-End Network Slicing for Manufacturing

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The manufacturing sector demands end-to-end network slicing (E2ENS) to meet diverse operational requirements such as high availability, guaranteed bandwidth, and ultra-low latency. However, current private 5G networks lack clearly defined service-level agreements (SLAs) to support transparent, traceable, secure, and efficient automation of E2ENS. This research introduces a blockchain-enabled approach to automate E2ENS through the use of smart contracts. These contracts establish tamper-proof, transparent agreements among key stakeholders – including mobile network operators, telecom regulators, and end users – tailored to specific manufacturing needs. The project will develop and implement smart contracts for two E2ENS scenarios, each governed by pre-defined SLAs. Upon fulfilment of the contractual conditions, the network slices will be instantiated automatically to meet the required use case parameters. This approach enables dynamic, demand-driven allocation of network resources over shared infrastructure, allowing rapid adaptation to evolving industrial requirements.

ECO-RAN: Energy Consumption Optimization in O-RAN

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While 5G is more energy-efficient than previous generations, its deployment has led to a significant increase in overall network energy consumption due to the advanced features required to support new services. As a result, reducing energy use has become a critical challenge for network operators, both from environmental and cost perspectives. To address this, the O-RAN Alliance is developing control mechanisms to manage energy-saving features in multi-vendor environments, though the design of specific energy-saving algorithms is intentionally left open for vendor innovation. AI and machine learning are expected to play a pivotal role, enabling networks to learn from historical data, adapt to changing conditions, and make automated control decisions. The ECO-RAN project focuses on optimizing energy consumption in a Non-Standalone (NSA) deployment, where 5G cells providing the capacity layer are co-located with 4G sites offering coverage. Preliminary analysis suggests that switching off 5G cells can lead to substantial energy savings; however, migrating 5G-connected users to 4G cells may degrade the quality of experience.To tackle this trade-off, ECO-RAN will develop innovative rApps and xApps to proactively balance energy efficiency and service quality. A RAN Digital Twin, leveraging the Keysight RIC Tester from the 6G-SANDBOX Malaga platform and data from a real Spanish MNO, will be built to validate these energy-saving strategies.

PQC-6G

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Pending